Journal of Electrical and Electronic Engineering 2014; 2(4): 55-63 Published online November 14, 2014 (http://www.sciencepublishinggroup.com/j/jeee) doi: 10.11648/j.jeee.20140204.11 ISSN: 2329-1613 (Print); ISSN: 2329-1605 (Online) LabVIEW based design implementation of M-PSK transceiver using multiple Forward Error Correction coding technique for Software Defined Radio applications Nikhil Marriwala 1 , Om Prakash Sahu 2 , Anil Vohra 3 1 Electronics & Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India 2 Electronics & Communication Engineering Department, National Institute of Technology, Kurukshetra, India 3 Electronics & Science Department, Kurukshetra University, Kurukshetra, India Email address: [email protected] (N. Marriwala), [email protected] (O. P. Sahu), [email protected] (A. Vohra) To cite this article: Nikhil Marriwala, Om Prakash Sahu, Anil Vohra. LabVIEW Based Design Implementation of M-PSK Transceiver Using Multiple Forward Error Correction Coding Technique for Software Defined Radio Applications. Journal of Electrical and Electronic Engineering. Vol. 2, No. 4, 2014, pp. 55-63. doi: 10.11648/j.jeee.20140204.11 Abstract: Software-Defined Radio (SDR) is an enabling technology which is useful in a wide range of areas within wireless systems. SDR offers a perfect solution to the problem of spectrum scarcity in wireless communication. With the significant increase in the demand for reliable, high data rate transmission these days, a different number of modulation techniques need to be adopted. The main objective of this paper is to design and analyze an SDR based M-Phase Shift Keying (PSK) transceiver using LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) and to measure the Bit Error Rate (BER) in the presence of Additive White Gaussian Noise (AWGN) introduced in the channel. Forward Error Correction (FEC) is used as a channel coding scheme in this paper. FEC codes are used where the re-transmission of the data is not feasible, thus redundant bits are added along with the message bits and transmitted through the channel. This paper describes the fundamental concept for the design & development of an SDR -based transceiver simulation model under PSK Scheme & analyses the performance of two Forward Error Correction channel coding algorithms namely the Convolution and the Turbo Codes. In this paper we have shown that how fast and effectively we can build a PSK transceiver for interactive Software Defined Radio. With the help of this design we are able to see and prove that data errors can be minimized using coding techniques, which in turn improves the Signal to noise ratio (SNR). Keywords: Software Defined Radio, Bit Error Rate, Additive White Gaussian Noise, Phase Shift Keying, Signal-To-Noise Ratio, Forward Error Correction 1. Introduction The term Software Defined Radio refers to reconfigurable or reprogrammable radio that shows different functionality with the same hardware. The entire functionality of the SDR can be defined in software [1]. The aim of this paper is to simulate SDR for next generation wireless communication systems by using the M-PSK modulation technique in LabVIEW. SDR provides an alternative to systems such as the third generation (3G) and the fourth generation (4G) systems [2]. A Complete hardware based system has many limitations. SDR technology provides many benefits including increased interoperability, reduced cost, and improved life cycle for communication systems [1, 2]. SDR’s can be reconfigured and can talk and listen to multiple channels at the same time. The transmitter of an SDR system converts digital signals to analog waveforms. The analog waveforms generated are then transmitted to the receiver. The received analog waveforms are then down converted, sampled, and demodulated using software on a reconfigurable baseband processor [3]. SDR systems can be used in ubiquitous network environments because of its flexibility and programmability [4, 5]. The use of digital signals reduces hardware, noise and interference problems as compared to the analogue signal in transmission, which is one of the main advantages of digital transmission [6, 7].
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Journal of Electrical and Electronic Engineering 2014; 2(4): 55-63
Published online November 14, 2014 (http://www.sciencepublishinggroup.com/j/jeee)
doi: 10.11648/j.jeee.20140204.11
ISSN: 2329-1613 (Print); ISSN: 2329-1605 (Online)
LabVIEW based design implementation of M-PSK transceiver using multiple Forward Error Correction coding technique for Software Defined Radio applications
Nikhil Marriwala1, Om Prakash Sahu
2, Anil Vohra
3
1Electronics & Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University,
Kurukshetra, India 2Electronics & Communication Engineering Department, National Institute of Technology, Kurukshetra, India 3Electronics & Science Department, Kurukshetra University, Kurukshetra, India
Raised cosine Filter: The raised cosine filter is one of the
most common pulse-shaping filters in communications
systems. In addition, it is used to minimize inter symbol
interference (ISI).
Root Raised Cosine Filter: The root raised cosine filter at
low frequency produces a frequency response with unity gain
and complete at higher frequencies.
Gaussian filter: The Gaussian pulse-shaping filter reduces
the levels of side-lobes of the PSK & GMSK spectrum.
4. Bit Error Rate (BER) &
Signal-to-Noise Ratio (SNR)
Figure 13. BER Vs Eb/N0(db) (4,8,16,32,64,128,256 bit FSK) Output Results
for Convolution coding.
Bit Error Rate (BER): In this section we discuss the BER
Vs the SNR achieved for different M-PSK in a noisy
channel for both the Convolution and Turbo coding. The
bit error rate (BER) is the number of bit errors divided by the
total number of transferred bits during a considered time
interval. BER is a unit less performance measure which is
often expressed as a percentage (%). A pseudorandom data
sequence (15) is used for the analysis in this design. The
BER parameter represents the current operating BER of a
specific modulation type and in this design the modulation
scheme selected is M-PSK. This value depends on various
channel characteristics, including the transmit power and
noise level.
Figure 14. BER Vs Eb/N0(db) (4,8,16,32,64,128,256 bit FSK) Output Results
for Turbo coding .
5. Simulation Results & of PSK
Transceiver Using Labview
In this section we describe the simulation results of M-
PSK transceiver system for a noisy channel. BER Vs
Eb/N0(db) for (4,8,16,32,64,128,256 bit PSK) has been given
in Figure 13 & Figure 14. Output Results for Convolution
coding and Turbo coding has been been illustrated with the
PSK parameters for Simulation being described in Table: 1.
By taking a look at the output results we can very clearly say
that Turbo coding gives a much improved and better
minimization of the data errors than the Convolution coding.
The simulation results conclude that minimum BER
achieved using Turbo coding is in the range of 10-8
as
compared to that of Convolution which is in the range (10-7
)
at a particular value of SNR. Hence, even at larger values of
63 Nikhil Marriwala et al.: LabVIEW Based Design Implementation of M-PSK Transceiver Using Multiple Forward Error Correction
Coding Technique for Software Defined Radio Applications
SNR, the BER achieved is extremely small. With the help of
this design we can also show that how fast and effectively we
can build a PSK transceiver for Software Defined Radio.
6. Conclusion
In this section we discuss the simulation results of the M-
PSK transceiver VI for noisy channel. From the results it
becomes clear that the wireless system designed based on
PSK technique provide high data rate and SNR. This can be
very clearly seen in terms of the BER Vs Eb/No output graph.
We can also see very clearly with these results that data
errors can be minimized using coding techniques, which in
turn improves the Signal to noise ratio (SNR) further, we can
also say looking at the results that Turbo coding gives a
much improved and better minimization of the data errors
that the Convolution & Viterbi coding. The performance of
M-level PSK systems ( 4,8,16,32,64,128,256) for additive
white Gaussian noise channel has been evaluated and
compared on the basis of the simulations in LabVIEW as
shown in Figure 13 & Figure 14. In this paper we have
shown that how fast and effectively we can build a PSK
transceiver for Software Defined Radio. We have used the
Graphical programming language LabVIEW for building a
PSK transceiver system which consists of a message source,
a pulse shape filter, a modulator on the Transmitter section
and demodulator, a frame synchronizer, a phase continuity
and frequency deviation on the Receiver section. With the
help of LabVIEW an interactive Software Defined Radio
system has been built in a shorter time as compared to other
text-based programming languages. With the help of this
design we are able to see and prove that data errors can be
minimized using coding techniques, which in turn improves
the Signal to noise ratio (SNR). Also we can say by looking
at the results that Turbo coding gives a much improved and
better minimization of the data errors than the Convolution
coding. In the end, we can say that the signal can be
recovered with very less probability of error in Turbo coding
than in Convolution coding with the increase in the M
(number of levels) at the destination.
References
[1] J. Mitola III,”Software Radios –Survey, Critical Evaluation and Future Directions,” in Proc. National Telesystems Conference, 1992, pp. 13/1513/23.
[2] Matthew N. O. Sadiku and Cajetan M. Akujuobi "Software-defined Radio: A brief Overview", IEEE Potentials Journal, October/November 2004, pg. 14-15.
[3] Wipro Technologies Innovative Solutions, Quality Leadership “Software-Defined Radio” White Paper: A Technology Overview, August 2002.
[4] Nikhil Marriwala, O. P. Sahu, Anil Vohra,: “8-QAM Software Defined Radio Based Approach for Channel
Encoding and Decoding Using Forward Error Correction”, Wireless Personal Communications, 1st May-2013, Springer US, 10.1007/s11277-013-1191-z.
[5] Nikhil Marriwala, O. P. Sahu, Ritu Khullar and Anil Vohra, “Software Defined Radio (SDR) 4-bit QAM Modem using LabVIEW for Gaussian Channel”CIIT International Journal of Wireless Communication”. March 2011.
[6] C. Berrou, A. Glavieux, and P. Thitimajshima. Near Shannon limit error correcting coding and decoding: Turbo codes. In Proceedings of the IEEE International Conference on Communications, Geneva, Switzerland, May 2003.
[7] W. Tuttlebee, Software Defined Radio: Baseband Technologies for 3G Handsets and Base Stations, John Wiley & Sons, 2004.
[8] Friedrich K. Jondral “Software-Defined Radio—Basics and Evolution to Cognitive Radio” (EURASIP Journal on Wireless Communications and Networking 2005:3, 275–283).
[9] N. KIM, N. KEHTARNAVAZ, and M. TORLAK LabVIEW-Based Software-Defined Radio: 4-QAM Modem Proceedings of ICASSP, vol. 2, 2006, pp. 985-988.
[10] Eric Nicollet and Lee Pucker, “Standardizing Transceiver APIs for Software Defined and Cognitive Radio”, www.rfdesign.com, February 2008,
[11] P. Burns, “Software Defined Radio for 3G”, Artech House, 2002. ISBN 1-58053-347-7.
[12] Amanpreet Singh Saini, “The Automated Systems For Spectrum Occupancy Measurement And Channel Sounding In Ultra-Wideband, Cognitive, Communication, And Networking” Master of Science in Electrical Engineering, August 2009.
[13] RituKhullar, Sippy Kapoor, Naval Dhawan, “Modulation technique For Cognitive Radio, Applications”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 3, May-Jun 2012, pp. 123- 125.
[14] Hiroyasu Ishikawa, “Software Defined Radio Technology for Highly Reliable Wireless Communications,” Wireless Personal Communications, 64 (2012), 461–72 dx.doi.org/10.1007/s11277-012-0596-4.
[15] P. Prakasam and M. Madheswaran, “Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments,” Wireless Personal Communications, 50 (2008), 509–28 ,dx.doi.org/10.1007/s11277-008-9621-z.
[16] Ying Chen and Linda M. Davis, “A Cross-Layer Adaptive Modulation and Coding Scheme for Energy Efficient Software Defined Radio,” Journal of Signal Processing Systems, 69 (2011), 23–30,dx.doi.org/10.1007/s11265-011-0644-4.
[17] Shu-Ming Tseng, Yueh-Teng Hsu and Hong-Kung Lin, “Iterative Channel Decoding for PC-Based Software Radio DVB-T Receiver,” Wireless Personal Communications, 69 (2012), 403–11, dx.doi.org/10.1007/s11277-012-0580-z.